Regret Guarantees for Item-Item Collaborative Filtering
Abstract
There is much empirical evidence that item-item collaborative filtering works well in practice. Motivated to understand this, we provide a framework to design and analyze various recommendation algorithms. The setup amounts to online binary matrix completion, where at each time a random user requests a recommendation and the algorithm chooses an entry to reveal in the user's row. The goal is to minimize regret, or equivalently to maximize the number of +1 entries revealed at any time. We analyze an item-item collaborative filtering algorithm that can achieve fundamentally better performance compared to user-user collaborative filtering. The algorithm achieves good "cold-start" performance (appropriately defined) by quickly making good recommendations to new users about whom there is little information.
Cite
@article{arxiv.1507.05371,
title = {Regret Guarantees for Item-Item Collaborative Filtering},
author = {Guy Bresler and Devavrat Shah and Luis F. Voloch},
journal= {arXiv preprint arXiv:1507.05371},
year = {2016}
}